Information Inference And Decision

Download Information Inference And Decision full books in PDF, epub, and Kindle. Read online Information Inference And Decision ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Information Theory, Inference and Learning Algorithms

Author : David J. C. MacKay
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)

DOWNLOAD NOW!

Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

An Introduction to Bayesian Inference and Decision

Author : Robert L. Winkler
Publisher : Probabilistic Pub
ISBN 13 : 9780964793842
Total Pages : 452 pages
Book Rating : 4.7/5 (938 download)

DOWNLOAD NOW!

Book Synopsis An Introduction to Bayesian Inference and Decision by : Robert L. Winkler

Download or read book An Introduction to Bayesian Inference and Decision written by Robert L. Winkler and published by Probabilistic Pub. This book was released on 2003-01-01 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: CD-ROM contains: Beta Distribution Generator (Excel file) ; Binomial Distribution Generator (Excel file) ; book exercises (MS Word files) ; book figures (Powerpoint files) ; TreeAge Data decision trees for some of the examples in the book ; Demonstration versions of TreeAge Data and Lumina Analytica.

Statistical Decision Rules and Optimal Inference

Author : N. N. Cencov
Publisher : American Mathematical Soc.
ISBN 13 : 9780821813478
Total Pages : 514 pages
Book Rating : 4.8/5 (134 download)

DOWNLOAD NOW!

Book Synopsis Statistical Decision Rules and Optimal Inference by : N. N. Cencov

Download or read book Statistical Decision Rules and Optimal Inference written by N. N. Cencov and published by American Mathematical Soc.. This book was released on 2000-04-19 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: None available in plain English.

Statistical Inference as Severe Testing

Author : Deborah G. Mayo
Publisher : Cambridge University Press
ISBN 13 : 1108563309
Total Pages : 503 pages
Book Rating : 4.1/5 (85 download)

DOWNLOAD NOW!

Book Synopsis Statistical Inference as Severe Testing by : Deborah G. Mayo

Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Statistical and Inductive Inference by Minimum Message Length

Author : C.S. Wallace
Publisher : Springer Science & Business Media
ISBN 13 : 9780387237954
Total Pages : 456 pages
Book Rating : 4.2/5 (379 download)

DOWNLOAD NOW!

Book Synopsis Statistical and Inductive Inference by Minimum Message Length by : C.S. Wallace

Download or read book Statistical and Inductive Inference by Minimum Message Length written by C.S. Wallace and published by Springer Science & Business Media. This book was released on 2005-05-26 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the ‘best’ explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data. This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science. Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining. C.S. Wallace was appointed Foundation Chair of Computer Science at Monash University in 1968, at the age of 35, where he worked until his death in 2004. He received an ACM Fellowship in 1995, and was appointed Professor Emeritus in 1996. Professor Wallace made numerous significant contributions to diverse areas of Computer Science, such as Computer Architecture, Simulation and Machine Learning. His final research focused primarily on the Minimum Message Length Principle.

An Introduction to Bayesian Inference and Decision

Author : Robert L. Winkler
Publisher : Holt McDougal
ISBN 13 :
Total Pages : 584 pages
Book Rating : 4.:/5 (318 download)

DOWNLOAD NOW!

Book Synopsis An Introduction to Bayesian Inference and Decision by : Robert L. Winkler

Download or read book An Introduction to Bayesian Inference and Decision written by Robert L. Winkler and published by Holt McDougal. This book was released on 1972 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Theory of Statistical Inference and Information

Author : Igor Vajda
Publisher : Springer
ISBN 13 :
Total Pages : 440 pages
Book Rating : 4.:/5 (42 download)

DOWNLOAD NOW!

Book Synopsis Theory of Statistical Inference and Information by : Igor Vajda

Download or read book Theory of Statistical Inference and Information written by Igor Vajda and published by Springer. This book was released on 1989-02-28 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Reinforcement and Systemic Machine Learning for Decision Making

Author : Parag Kulkarni
Publisher : John Wiley & Sons
ISBN 13 : 1118271556
Total Pages : 324 pages
Book Rating : 4.1/5 (182 download)

DOWNLOAD NOW!

Book Synopsis Reinforcement and Systemic Machine Learning for Decision Making by : Parag Kulkarni

Download or read book Reinforcement and Systemic Machine Learning for Decision Making written by Parag Kulkarni and published by John Wiley & Sons. This book was released on 2012-07-11 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.

Algorithms for Decision Making

Author : Mykel J. Kochenderfer
Publisher : MIT Press
ISBN 13 : 0262047012
Total Pages : 701 pages
Book Rating : 4.2/5 (62 download)

DOWNLOAD NOW!

Book Synopsis Algorithms for Decision Making by : Mykel J. Kochenderfer

Download or read book Algorithms for Decision Making written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2022-08-16 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Bayesian Inference and Decision Techniques

Author : P. K. Goel
Publisher : North Holland
ISBN 13 :
Total Pages : 512 pages
Book Rating : 4.:/5 (319 download)

DOWNLOAD NOW!

Book Synopsis Bayesian Inference and Decision Techniques by : P. K. Goel

Download or read book Bayesian Inference and Decision Techniques written by P. K. Goel and published by North Holland. This book was released on 1986 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians from several countries. Part I of this book relates most directly to de Finetti's interests whilst Part II deals specifically with the implications of the assumption of finitely additive probability. Parts III & IV discuss applications of Bayesian methodology in econometrics and economic forecasting, and Part V examines assessment of prior parameters in specific parametric setting and foundational issues in probability assessment. The following section deals with state of the art for comparing probability functions and gives an assessment of prior distributions and utility functions. In Parts VII & VIII are a collection of papers on Bayesian methodology for general linear models and time series analysis (the most often used tools in economic modelling), and papers relevant to modelling and forecasting. The remaining two Parts examine, respectively, optimality considerations and the effectiveness of the Conditionality-Likelihood Principle as a vehicle to convince the non-Bayesians about the usefulness of the Bayesian paradigm.

Statistical Decision Theory and Bayesian Analysis

Author : James O. Berger
Publisher : Springer Science & Business Media
ISBN 13 : 147574286X
Total Pages : 633 pages
Book Rating : 4.4/5 (757 download)

DOWNLOAD NOW!

Book Synopsis Statistical Decision Theory and Bayesian Analysis by : James O. Berger

Download or read book Statistical Decision Theory and Bayesian Analysis written by James O. Berger and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.

The Design Inference

Author : William A. Dembski
Publisher : Cambridge University Press
ISBN 13 : 0521623871
Total Pages : 266 pages
Book Rating : 4.5/5 (216 download)

DOWNLOAD NOW!

Book Synopsis The Design Inference by : William A. Dembski

Download or read book The Design Inference written by William A. Dembski and published by Cambridge University Press. This book was released on 1998-09-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a reliable method for detecting intelligent causes: the design inference.The design inference uncovers intelligent causes by isolating the key trademark of intelligent causes: specified events of small probability. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative book shows how incomplete undirected causes are for science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians.

The Cambridge Handbook of the Law of Algorithms

Author : Woodrow Barfield
Publisher : Cambridge University Press
ISBN 13 : 1108663184
Total Pages : 1327 pages
Book Rating : 4.1/5 (86 download)

DOWNLOAD NOW!

Book Synopsis The Cambridge Handbook of the Law of Algorithms by : Woodrow Barfield

Download or read book The Cambridge Handbook of the Law of Algorithms written by Woodrow Barfield and published by Cambridge University Press. This book was released on 2020-11-05 with total page 1327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental building block of artificial intelligence - and, increasingly, society - but our legal institutions have largely failed to recognize or respond to this reality. The Cambridge Handbook of the Law of Algorithms, which features contributions from US, EU, and Asian legal scholars, discusses the specific challenges algorithms pose not only to current law, but also - as algorithms replace people as decision makers - to the foundations of society itself. The work includes wide coverage of the law as it relates to algorithms, with chapters analyzing how human biases have crept into algorithmic decision-making about who receives housing or credit, the length of sentences for defendants convicted of crimes, and many other decisions that impact constitutionally protected groups. Other issues covered in the work include the impact of algorithms on the law of free speech, intellectual property, and commercial and human rights law.

Info-Gap Decision Theory

Author : Yakov Ben-Haim
Publisher : Elsevier
ISBN 13 : 0080465706
Total Pages : 385 pages
Book Rating : 4.0/5 (84 download)

DOWNLOAD NOW!

Book Synopsis Info-Gap Decision Theory by : Yakov Ben-Haim

Download or read book Info-Gap Decision Theory written by Yakov Ben-Haim and published by Elsevier. This book was released on 2006-10-11 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. Info-Gap Decision Theory is written for decision analysts. The term "decision analyst" covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts, usually without calling themselves by this name. In addition to engineers, decision analysts work in planning offices for public agencies, in project management consultancies, they are engaged in manufacturing process planning and control, in financial planning and economic analysis, in decision support for medical or technological diagnosis, and so on and on. Decision analysts provide quantitative support for the decision-making process in all areas where systematic decisions are made. This second edition entails changes of several sorts. First, info-gap theory has found application in several new areas - especially biological conservation, economic policy formulation, preparedness against terrorism, and medical decision-making. Pertinent new examples have been included. Second, the combination of info-gap analysis with probabilistic decision algorithms has found wide application. Consequently "hybrid" models of uncertainty, which were treated exclusively in a separate chapter in the previous edition, now appear throughout the book as well as in a separate chapter. Finally, info-gap explanations of robust-satisficing behavior, and especially the Ellsberg and Allais "paradoxes", are discussed in a new chapter together with a theorem indicating when robust-satisficing will have greater probability of success than direct optimizing with uncertain models. - New theory developed systematically - Many examples from diverse disciplines - Realistic representation of severe uncertainty - Multi-faceted approach to risk - Quantitative model-based decision theory

Advanced Lectures on Machine Learning

Author : Olivier Bousquet
Publisher : Springer
ISBN 13 : 3540286500
Total Pages : 249 pages
Book Rating : 4.5/5 (42 download)

DOWNLOAD NOW!

Book Synopsis Advanced Lectures on Machine Learning by : Olivier Bousquet

Download or read book Advanced Lectures on Machine Learning written by Olivier Bousquet and published by Springer. This book was released on 2011-03-22 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Local Induction

Author : R. Bogdan
Publisher : Springer Science & Business Media
ISBN 13 : 9401197997
Total Pages : 353 pages
Book Rating : 4.4/5 (11 download)

DOWNLOAD NOW!

Book Synopsis Local Induction by : R. Bogdan

Download or read book Local Induction written by R. Bogdan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: The local justification of beliefs and hypotheses has recently become a major concern for epistemologists and philosophers of induction. As such, the problem of local justification is not entirely new. Most pragmatists had addressed themselves to it, and so did, to some extent, many classical inductivists in the Bacon-Whewell-Mill tradition. In the last few decades, however, the use of logic and semantics, probability calculus, statistical methods, and decision-theoretic concepts in the reconstruction of in ductive inference has revealed some important technical respects in which inductive justification can be local: the choice of a language, with its syntactic and semantic features, the relativity of probabilistic evalua tions to an initial body of evidence or background knowledge and to an agent's utilities and preferences, etc. Some paradoxes and difficulties encountered by purely formal accounts of inductive justification, the erosion of the once dominant empiricist position, which most approaches to induction took for granted, and the increasing challenge of noninduc tivist epistemolgies have underscored the need of accounting for the methodological problems of applying inductive logic to real life contexts, particularly in science. As a result, in the late fifties and sixties, several related developments pointed to a new, local approach to inductive justification.

Order Statistics & Inference

Author : Narayanaswamy Balakrishnan
Publisher : Elsevier
ISBN 13 : 1483297497
Total Pages : 399 pages
Book Rating : 4.4/5 (832 download)

DOWNLOAD NOW!

Book Synopsis Order Statistics & Inference by : Narayanaswamy Balakrishnan

Download or read book Order Statistics & Inference written by Narayanaswamy Balakrishnan and published by Elsevier. This book was released on 2014-06-28 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The literature on order statistics and inferenc eis quite extensive and covers a large number of fields ,but most of it is dispersed throughout numerous publications. This volume is the consolidtion of the most important results and places an emphasis on estimation. Both theoretical and computational procedures are presented to meet the needs of researchers, professionals, and students. The methods of estimation discussed are well-illustrated with numerous practical examples from both the physical and life sciences, including sociology,psychology,a nd electrical and chemical engineering. A complete, comprehensive bibliography is included so the book can be used both aas a text and reference.